Florian Vandecasteele, Krishna Kumar, Kenzo Milleville, & Steven Verstockt. (2019). Video Summarization And Video Highlight Selection Tools To Facilitate Fire Incident Management. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram. Abstract: This paper reports on the added value of combining different types of sensor data and geographic information for fire incident management. A survey was launched within the Belgian fire community to explore the need of added value and the use of new types of sensor data during a fire incident. This evaluation revealed that people are visually-oriented and that video footages and images are of great value to gain insights in a particular problem. However, due to the limited available time (i.e., fast decisions need to be taken) and the large amount of cameras it is not feasible to analyze all video footages sequentially. To solve this problem we propose a video summarization mechanism and a video highlight selection tool based on the automatic generated image and video tags. Keywords: machine learning, video data, filtering, crisis management, user-evaluation Track: T10- Knowledge, Semantics and AI for RISK and CRISIS management Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML

Abstract: This paper reports on the added value of combining different types of sensor data and geographic information for fire incident management. A survey was launched within the Belgian fire community to explore the need of added value and the use of new types of sensor data during a fire incident. This evaluation revealed that people are visually-oriented and that video footages and images are of great value to gain insights in a particular problem. However, due to the limited available time (i.e., fast decisions need to be taken) and the large amount of cameras it is not feasible to analyze all video footages sequentially. To solve this problem we propose a video summarization mechanism and a video highlight selection tool based on the automatic generated image and video tags.

Keywords: machine learning, video data, filtering, crisis management, user-evaluation

Track: T10- Knowledge, Semantics and AI for RISK and CRISIS management

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| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML